from flyai.utils import remote_helper

import torch
import torch.nn as nn
import torch.nn.functional as F
# from torchvision.models import ResNet


from config import args
from path import *
from logger import logger
from resnest import *
from DenseNet import DenseNet
from efficientnet import EfficientNet
from hub import load_state_dict_from_url
from losses import ArcMarginProduct

import re
from urllib.request import urlretrieve


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=args.class_num, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.drop = nn.Dropout(args.dropout_rate)
        self.conv1d = nn.Sequential(nn.Dropout(args.dropout_rate),
                                    # nn.Linear(512 * block.expansion, 256),
                                    # nn.BatchNorm1d(256),
                                    # nn.Dropout(args.dropout_rate),
                                    )
        if args.use_arc:
            self.myfc = ArcMarginProduct(512 * block.expansion, num_classes)
        else:
            self.myfc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x, label=None):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.conv1d(x)
        if args.use_arc and label is not None:
            x = self.myfc(x, label)
        else:
            x = self.myfc(x)

        if not args.use_focal:
            x = torch.softmax(x, dim=1)

        return x


def _resnet(block):
    model = ResNet(Bottleneck, block)
    return model


def freeze_model(model, freeze_rate=args.freeze_rate):
    assert 0 <= freeze_rate <= 1

    layers = [c for c in model.modules() if len(list(c.modules())) == 1]
    n = len(layers)
    n_freeze = int(freeze_rate * n)
    logger.logger.info(f'{n} layers in total, freeze {n_freeze} layers')

    for i, c in enumerate(layers):
        for param in c.parameters():
            param.requires_grad = False if i < freeze_rate else True


def unfreeze_model(model):
    logger.logger.info('unfreeze model')
    for param in model.parameters():
        param.requires_grad = True


def _load_state_dict_densenet(model_url='', state_dict=None):
    # '.'s are no longer allowed in module names, but previous _DenseLayer
    # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
    # They are also in the checkpoints in model_urls. This pattern is used
    # to find such keys.
    assert model_url or state_dict

    pattern = re.compile(
        r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')

    if state_dict is None:
        state_dict = load_state_dict_from_url(model_url)

    for key in list(state_dict.keys()):
        res = pattern.match(key)
        if res:
            new_key = res.group(1) + res.group(2)
            state_dict[new_key] = state_dict[key]
            del state_dict[key]

    return state_dict


def init_model(model: nn.Module):
    for m in model.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight)
            nn.init.kaiming_normal_(m.bias)


def get_model_by_name(model_name: str, pretrain=args.pretrain):
    from_flyai_name = ['resnest50', 'efficientnetb3']
    model_list = {
        'resnet50': '_resnet(block=[3, 4, 6, 3])',
        'resnest50': 'resnest50()',
        'densenet121': 'DenseNet(32, (6, 12, 24, 16), 64)',
        'efficientnetb4': "EfficientNet.from_name('efficientnet-b4')",
        'efficientnetb3': "EfficientNet.from_name('efficientnet-b3')"
    }
    assert model_name in model_list
    logger.logger.info(f'load model: {args.prefix}{model_name}{args.suffix}, pretrain = {pretrain}')

    if not pretrain:
        model = eval(model_list[model_name])
        # init_model(model)
    else:
        if args.train_flyai:
            logger.logger.info(model_name)
            if model_name in from_flyai_name:
                pretrained_path = remote_helper.get_remote_date(eval(f'{model_name}_path'))
                logger.logger.debug(pretrained_path)
                pretrained_state_dict = torch.load(pretrained_path)
            else:
                if 'densenet' in model_name:
                    pretrained_state_dict = _load_state_dict_densenet(eval(f'{model_name}_url'))
                else:
                    filename = os.path.basename(eval(f'{model_name}_url')) + '.pth'
                    filepath = os.path.join(MODEL_PATH, filename)
                    urlretrieve(eval(f'{model_name}_url'), filepath)
                    pretrained_state_dict = torch.load(filepath)

        else:
            pretrained_path = eval(f'{model_name}_path')
            pretrained_state_dict = torch.load(pretrained_path)
            if 'densenet' in model_name:
                pretrained_state_dict = _load_state_dict_densenet(state_dict=pretrained_state_dict)

        model = eval(model_list[model_name])
        model_state_dict = model.state_dict()
        pretrained_state_dict = {k: v for k, v in pretrained_state_dict.items() if k in model_state_dict}
        model_state_dict.update(pretrained_state_dict)
        model.load_state_dict(model_state_dict)

    freeze_model(model)
    return model.cuda()


def get_models(multi_model=args.multi_model, pretrain=args.pretrain):

    model1 = args.model1
    if multi_model:
        model2 = args.model2
        return get_model_by_name(model1, pretrain), get_model_by_name(model2, pretrain)
    else:
        return get_model_by_name(model1, pretrain)


if __name__ == '__main__':
    _, net = get_models(multi_model=True, pretrain=True)
    # print(1)
    # unfreeze_model(net1)
    # unfreeze_model(net2)

    # new_net1 = torch.nn.Sequential(*(list(net.children())[:-1]))
    # new_net1.add_module('myfc', nn.Sequential(nn.Linear(512 * 4, 2),
    #                             nn.Dropout(args.dropout_rate)))
    #
    # new_net1.add_module('softmax', nn.Softmax(dim=1))
    # net.load_state_dict(torch.load(r'F:\FlyAI\BaldModel\model11.pt'))
    # new_net1 = new_net1.cuda()

    x = torch.randn((2, 3, 128, 128)).cuda()
    y = net(x)
    # logger.logger.debug(y)
